Near-Automated Estimate of City Nitrogen Oxides Emissions Applied to
South and Southeast Asia
Abstract
Cities in South and Southeast Asia are developing rapidly without
routine, up-to-date knowledge of air pollutant precursor emissions. This
data deficit can potentially be addressed for nitrogen oxides (NOx) by
deriving city NOx emissions from satellite observations of nitrogen
dioxide (NO2) sampled under windy conditions. NO2 plumes of isolated
cities are aligned along a consistent wind-rotated direction and a
best-fit Gaussian is applied to estimate emissions. This approach
currently relies on non-standardized selection of the area to sample
around the city centre and Gaussian fits often fail or yield
non-physical parameters. Here, we automate this approach by defining
many (54) sampling areas that we test with TROPOspheric Monitoring
Instrument (TROPOMI) NO2 observations for 2019 over 19 cities in South
and Southeast Asia. Our approach is efficient, adaptable to many cities,
standardizes and eliminates sensitivity of the Gaussian fit to sampling
area choice, and increases success of deriving annual emissions from
40-60% with one sampling area to 100% (all 19 cities) with 54. The
annual emissions we estimate range from 16±5 mol s-1 for Yangon
(Myanmar) and Bangalore (India) to 125±41 mol s-1 for Dhaka
(Bangladesh). With the enhanced success of our approach, we find
evidence from comparison of our top-down emissions to past studies and
to inventory estimates that the wind rotation and EMG fit approach may
be biased, as it does not adequately account for spatial and seasonal
variability in NOx photochemistry. Further methodological development is
needed to enhance its accuracy and to exploit it to derive sub-annual
emissions.